Project: Investigating Ford's go-Bike Trips Data (2019)

Table of Contents

• Introduction
• Data Wrangling
• Exploratory Data Analysis
• Conclusions

Introduction

• I've chosen to explore and explain the ford go-bike data :)
• The data describes the trip activity of its users.
• Have fun reading this beatifully done analysis :)

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import statsmodels as sm
import seaborn as sns
%matplotlib inline
np.random.seed(42)

Data Wrangling

Tip: In this section of the report, I will load in the data, check for cleanliness, and then trim and clean the dataset for analysis.

General Properties

ford_data = pd.read_csv('201902-fordgobike-tripdata.csv')
ford_data.head()

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	32:10.1	01:56.0	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	-122.402923	4902	Customer	1984.0	Male	No
1	42521	53:21.8	42:03.1	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	-122.393170	2535	Customer	NaN	NaN	No
2	61854	13:13.2	24:08.1	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	Customer	1972.0	Male	No
3	36490	54:26.0	02:36.8	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	-122.444293	6638	Subscriber	1989.0	Other	No
4	1585	54:18.5	20:44.1	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974.0	Male	Yes

Well I don't think I am going to use the longitude nor the latitude in my analysis so i'm gonna drop those :)

ford_data.drop(columns = ['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude'], axis = 1, inplace = True)
ford_data.head()

	duration_sec	start_time	end_time	start_station_id	start_station_name	end_station_id	end_station_name	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	32:10.1	01:56.0	21.0	Montgomery St BART Station (Market St at 2nd St)	13.0	Commercial St at Montgomery St	4902	Customer	1984.0	Male	No
1	42521	53:21.8	42:03.1	23.0	The Embarcadero at Steuart St	81.0	Berry St at 4th St	2535	Customer	NaN	NaN	No
2	61854	13:13.2	24:08.1	86.0	Market St at Dolores St	3.0	Powell St BART Station (Market St at 4th St)	5905	Customer	1972.0	Male	No
3	36490	54:26.0	02:36.8	375.0	Grove St at Masonic Ave	70.0	Central Ave at Fell St	6638	Subscriber	1989.0	Other	No
4	1585	54:18.5	20:44.1	7.0	Frank H Ogawa Plaza	222.0	10th Ave at E 15th St	4898	Subscriber	1974.0	Male	Yes

ford_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 12 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   end_station_id           183215 non-null  float64
 6   end_station_name         183215 non-null  object 
 7   bike_id                  183412 non-null  int64  
 8   user_type                183412 non-null  object 
 9   member_birth_year        175147 non-null  float64
 10  member_gender            175147 non-null  object 
 11  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(3), int64(2), object(7)
memory usage: 11.9+ MB

ford_data.describe()

	duration_sec	start_station_id	end_station_id	bike_id	member_birth_year
count	183412.000000	183215.000000	183215.000000	183412.000000	175147.000000
mean	726.078435	138.590427	136.249123	4472.906375	1984.806437
std	1794.389780	111.778864	111.515131	1664.383394	10.116689
min	61.000000	3.000000	3.000000	11.000000	1878.000000
25%	325.000000	47.000000	44.000000	3777.000000	1980.000000
50%	514.000000	104.000000	100.000000	4958.000000	1987.000000
75%	796.000000	239.000000	235.000000	5502.000000	1992.000000
max	85444.000000	398.000000	398.000000	6645.000000	2001.000000

There appear to be alot of missing values. However it's understandable since it's in the gender & age columns as many people prefer to not disclose their gender nor their age, so we're gonna just fill those with the mean for each column :)

Also there is an outlier in the age column as someone decided to put their birth year 1878 :'D

ford_data['member_gender'].mode()

0    Male
dtype: object

sns.countplot(data = ford_data, x = 'member_gender');

ford_data['member_gender'].fillna(str(ford_data['member_gender'].mode()).replace(str(ford_data['member_gender'].mode()), 'Male'), inplace = True)

ford_data['member_birth_year'].fillna(ford_data['member_birth_year'].mean().__round__(), inplace = True)

ford_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 12 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   end_station_id           183215 non-null  float64
 6   end_station_name         183215 non-null  object 
 7   bike_id                  183412 non-null  int64  
 8   user_type                183412 non-null  object 
 9   member_birth_year        183412 non-null  float64
 10  member_gender            183412 non-null  object 
 11  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(3), int64(2), object(7)
memory usage: 11.9+ MB

ford_data.head(10)

	duration_sec	start_time	end_time	start_station_id	start_station_name	end_station_id	end_station_name	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	32:10.1	01:56.0	21.0	Montgomery St BART Station (Market St at 2nd St)	13.0	Commercial St at Montgomery St	4902	Customer	1984.0	Male	No
1	42521	53:21.8	42:03.1	23.0	The Embarcadero at Steuart St	81.0	Berry St at 4th St	2535	Customer	1985.0	Male	No
2	61854	13:13.2	24:08.1	86.0	Market St at Dolores St	3.0	Powell St BART Station (Market St at 4th St)	5905	Customer	1972.0	Male	No
3	36490	54:26.0	02:36.8	375.0	Grove St at Masonic Ave	70.0	Central Ave at Fell St	6638	Subscriber	1989.0	Other	No
4	1585	54:18.5	20:44.1	7.0	Frank H Ogawa Plaza	222.0	10th Ave at E 15th St	4898	Subscriber	1974.0	Male	Yes
5	1793	49:58.6	19:51.8	93.0	4th St at Mission Bay Blvd S	323.0	Broadway at Kearny	5200	Subscriber	1959.0	Male	No
6	1147	55:35.1	14:42.6	300.0	Palm St at Willow St	312.0	San Jose Diridon Station	3803	Subscriber	1983.0	Female	No
7	1615	41:06.8	08:02.8	10.0	Washington St at Kearny St	127.0	Valencia St at 21st St	6329	Subscriber	1989.0	Male	No
8	1570	41:48.8	07:59.7	10.0	Washington St at Kearny St	127.0	Valencia St at 21st St	6548	Subscriber	1988.0	Other	No
9	1049	49:47.7	07:17.0	19.0	Post St at Kearny St	121.0	Mission Playground	6488	Subscriber	1992.0	Male	No

There is just some missing id's so we're gonna drop those :)

ford_data.dropna(axis = 0, inplace = True)
ford_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 183215 entries, 0 to 183411
Data columns (total 12 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183215 non-null  int64  
 1   start_time               183215 non-null  object 
 2   end_time                 183215 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   end_station_id           183215 non-null  float64
 6   end_station_name         183215 non-null  object 
 7   bike_id                  183215 non-null  int64  
 8   user_type                183215 non-null  object 
 9   member_birth_year        183215 non-null  float64
 10  member_gender            183215 non-null  object 
 11  bike_share_for_all_trip  183215 non-null  object 
dtypes: float64(3), int64(2), object(7)
memory usage: 13.3+ MB

Now The data is nice and clean :D

Exploratory Data Analysis

Research Question 1

$What$ $are$ $the$ $stations$ $with$ $the$ $longest$ $duration$ $?$

ford_data['start_station_name'].groupby(ford_data['duration_sec']).max().tail(10)

duration_sec
80891    Civic Center/UN Plaza BART Station (Market St ...
81549                               Jersey St at Church St
82385                                Fell St at Stanyan St
82512                                 Myrtle St at Polk St
83195                          Bancroft Way at College Ave
83407                                 11th St at Natoma St
83519                                  Page St at Scott St
83772                                  Folsom St at 9th St
84548         Powell St BART Station (Market St at 4th St)
85444         Powell St BART Station (Market St at 5th St)
Name: start_station_name, dtype: object

ford_data['end_station_name'].groupby(ford_data['duration_sec']).max().tail(10)

duration_sec
80891                               Webster St at Grove St
81549                        Cesar Chavez St at Dolores St
82385                                Fell St at Stanyan St
82512    Civic Center/UN Plaza BART Station (Market St ...
83195                           Telegraph Ave at Ashby Ave
83407                                        16th St Depot
83519    San Francisco Public Library (Grove St at Hyde...
83772                                Hubbell St at 16th St
84548                                 Myrtle St at Polk St
85444                               Valencia St at 16th St
Name: end_station_name, dtype: object

It appears from the previous tables that the longest recorded trips were between Powell St BART Station & both of (Myrtle St at Polk St & Valencia St at 16th St)

Research Question 2

$Does$ $the$ $user's$ $Gender$ $affect$ $duration$ $of$ $the$ $trip$ $?$

# for faster computation i'm gonna use random samples of range 20,000 of the dataset in the plots
sample = np.random.choice(ford_data.shape[0], 20000, replace = False)
ford_data_subset = ford_data.iloc[sample]

sns.countplot(data = ford_data, x = 'member_gender');

sns.catplot(data = ford_data_subset, x = 'member_gender', y = 'duration_sec');

From the previous graph it's clear that the gender of the user is not related to the duration of the trip, since all of the three columns nearly have the same height.

Research Question 3

$Does$ $the$ $user's$ $type$ ($Either$ $'Customer'$ $or$ $'Subescriber'$) $affect$ $the$ $duration$ $of$ $the$ $trip$ $?$

sns.countplot(data = ford_data, x = 'user_type');

sns.catplot(data = ford_data_subset, x = 'user_type', y = 'duration_sec');

From the previous graph it's clear that the type of the user (Either Customer or Subscriber) is not related to the duration of the trip, since the two columns nearly have the same height.

Research Question 4

$Does$ $the$ $user's$ $age$ $affect$ $the$ $duration$ $of$ $the$ $trip$ $that$ $user$ $takes$ $?$

plt.hist(ford_data['member_birth_year']);
plt.axvline(ford_data['member_birth_year'].mean().__round__(), color = 'red')

<matplotlib.lines.Line2D at 0x13da1970>

We need to adjust the bins for a better look :)

bins = np.arange(1930, ford_data['member_birth_year'].max(), 10)
plt.xlabel('Years of Birth')
plt.ylabel('Count')
plt.hist(ford_data['member_birth_year'], bins = bins);
plt.axvline(ford_data['member_birth_year'].mean().__round__(), color = 'red')
plt.axvline(ford_data['member_birth_year'].max(), color = 'black')
plt.legend(['Mean', 'Max']);
plt.show()

It's clear from the previous graph that most of the users are born between 1980 and 1990, which means the majority are adults between 31 to 41 years old.

sns.regplot(data = ford_data_subset, y = 'member_birth_year', x = 'duration_sec');

There appears a teeny tiny (expected) relation betweeen age and duration of the trip since the longer durations are from users of younger age (usually).

Research Question 5

$What$ $are$ $the$ $females$ $proportion$ $from$ $the$ $total$ $adults $from$ $the$ $previous$ $graph$ $?$

ttype_markers = [['Male', '^'], ['Female', 'p'], ['Other', 'o']]

for ttype, marker in ttype_markers:
    plot_data = ford_data_subset.loc[ford_data_subset['member_gender'] == ttype]
    sns.regplot(data = plot_data, x = 'duration_sec', y = 'member_birth_year', marker = marker, fit_reg = False)
plt.legend(['Males', 'Females', 'Other']);

The most durations recorded are nearly in the range of 100 ~ 2000 Seconds by the three genders :)

Conclusions

• Most of the users are Males.
• There is no apparent relation what so ever between user's gender and the duration of the trip.
• There is no apparent relation between user's type (Either Customer or Subscriber) and the duration of the trip.
• The most durations recorded are nearly in the range of 100 ~ 2000 Seconds by the three genders :)
• There appears a teeny tiny (expected) relation betweeen age and duration of the trip since the longer durations are from users of younger age (usually).
• Most of the users are born between 1980 and 1990, which means the majority are adults between 31 to 41 years old.

Limitations

• There is no enough parameters to know which actually affects most the duration of the trip.